Cuneo Data Analysis

This is an R Markdown Notebook. Each section of the code is then explained.

First of all import the libraries needed

#install.packages(c("datavolley", "ovlytics"))
library(datavolley)
library(ggplot2)

Avvertimento: il pacchetto ‘ggplot2’ è stato creato con R versione 4.3.2

library(dplyr)

Avvertimento: il pacchetto ‘dplyr’ è stato creato con R versione 4.3.2
Caricamento pacchetto: ‘dplyr’

I seguenti oggetti sono mascherati da ‘package:stats’:

    filter, lag

I seguenti oggetti sono mascherati da ‘package:base’:

    intersect, setdiff, setequal, union

library(ovlytics)

Import the file you are interested in considering more than one match, you have to import all the folder

filename <- "C:/Users/mirko/Documents/GitHub/CuneoWebsite.io/Assets/Cuneo-Bergamo_cuneo.dvw"
#d <- dir("C:/Users/mirko/OneDrive - Politecnico di Milano/Altro/Volley/Conco2324/Parella Torino/Ritorno/", pattern = "dvw$", full.names = TRUE)

teamName = 'HONDA OLIVERO S.BERNARDO CUNEO'
x <- dv_read(filename)

Avvertimento: stri_enc_detect2 is deprecated and will be removed in a future release of 'stringi'.

serve_idx <- find_serves(plays(x))
table(plays(x)$team[serve_idx])

HONDA OLIVERO S.BERNARDO CUNEO            VOLLEY BERGAMO 1991 
                            98                             93 

Funzioni utili

## find rows where a single player is on court
player_on_court <- function(x, target_player_id, team = NULL) {
  if (!is.null(team)) team <- match.arg(team, c("home", "visiting"))
  ## 'team' is optional here, if NULL then we look at both home and visiting teams
  idx <- rep(FALSE, nrow(x))
  if (is.null(team) || team == "home") {
    idx <- idx | x$home_player_id1 == target_player_id | x$home_player_id2 == target_player_id | x$home_player_id3 == target_player_id |
                 x$home_player_id4 == target_player_id | x$home_player_id5 == target_player_id | x$home_player_id6 == target_player_id
  }
  if (is.null(team) || team == "visiting") {
    idx <- idx | x$visiting_player_id1 == target_player_id | x$visiting_player_id2 == target_player_id | x$visiting_player_id3 == target_player_id |
                 x$visiting_player_id4 == target_player_id | x$visiting_player_id5 == target_player_id | x$visiting_player_id6 == target_player_id
  }
  idx[is.na(idx)] <- FALSE
  idx
}

## find rows where any of our target players are on court
any_player_on_court <- function(x, target_player_ids, team = NULL) {
  ## for each target player, find rows where they are on court
  out <- lapply(target_player_ids, function(pid) player_on_court(x, target_player_id = pid, team = team))
  ## and now find rows where ANY of those players were on court
  apply(do.call(cbind, out), 1, any)
}

## find rows where all of our target players are on court
all_players_on_court <- function(x, target_player_ids, team = NULL) {
  ## for each target player, find rows where they are on court
  out <- lapply(target_player_ids, function(pid) player_on_court(x, target_player_id = pid, team = team))
  ## and now find rows where ALL of those players were on court
  apply(do.call(cbind, out), 1, all)
}

d <- dir("C:/Users/mirko/Documents/GitHub/CuneoWebsite.io/Assets/", pattern = "dvw$", full.names = TRUE)
lx <- list()
## read each file
for (fi in seq_along(d)) lx[[fi]] <- dv_read(d[fi], insert_technical_timeouts = FALSE)

Avvertimento: stri_enc_detect2 is deprecated and will be removed in a future release of 'stringi'.

## now extract the play-by-play component from each and bind them together
px <- list()
for (fi in seq_along(lx)) px[[fi]] <- plays(lx[[fi]])
px <- do.call(rbind, px)

Rendimento in Battuta

#, end_zone == 5
table_data <- px %>% 
  dplyr::filter(skill == "Serve", team == teamName) %>% 
  group_by(player_name) %>% 
  dplyr::summarize(
    N_battute = n(),
    count_perfette = sum(evaluation_code == "#", na.rm = TRUE),
    count_positive = sum(evaluation_code == "+", na.rm = TRUE),
    count_escalamative = sum(evaluation_code == "!", na.rm = TRUE),
    count_negative = sum(evaluation_code == "-", na.rm = TRUE),
    count_errori = sum(evaluation_code == "=", na.rm = TRUE),
    positività = (count_positive + count_perfette)/N_battute,
    efficienza = (count_positive + count_perfette - count_errori)/N_battute,
  )

table_data

library(plotly)

Avvertimento: il pacchetto ‘plotly’ è stato creato con R versione 4.3.2
Caricamento pacchetto: ‘plotly’

Il seguente oggetto è mascherato da ‘package:ggplot2’:

    last_plot

Il seguente oggetto è mascherato da ‘package:stats’:

    filter

Il seguente oggetto è mascherato da ‘package:graphics’:

    layout

fig <- plot_ly(table_data, 
               x = ~positività, 
               y = ~efficienza,
               type = 'scatter', 
               mode = 'markers', 
               hovertemplate = paste('<i>Player</i>: %{text}',
                                     '<br><b>Positività</b>: %{x}',
                                     '<br><b>Efficienza</b>: %{y}',
                                     '<br><b>Battuta</b>: %{marker.size}<extra></extra>'),
               color = ~positività,
               marker = list(size = ~N_battute, sizemode = "area", sizeref = 0.005, opacity = 0.5),
               text = ~player_name
)

fig <- fig %>% layout(title = 'Qualità Battuta',
                      xaxis = list(title = 'Positività', showgrid = TRUE),
                      yaxis = list(title = 'Efficienza', showgrid = TRUE)
)

fig

library(htmlwidgets)

Avvertimento: il pacchetto ‘htmlwidgets’ è stato creato con R versione 4.3.2

Rendimento in Ricezione

Ora analizziamo la ricezione:

#, end_zone == 5
table_data <- px %>% 
  dplyr::filter(skill == "Reception", team == teamName) %>% 
  group_by(player_name) %>% 
  dplyr::summarize(
    N_receptions = n(),
    count_perfette = sum(evaluation_code == "#", na.rm = TRUE),
    count_positive = sum(evaluation_code == "+", na.rm = TRUE),
    count_escalamative = sum(evaluation_code == "!", na.rm = TRUE),
    count_negative = sum(evaluation_code == "-", na.rm = TRUE),
    count_errori = sum(evaluation_code == "=", na.rm = TRUE),
    positività = (count_positive + count_perfette)/N_receptions,
    efficienza = (count_positive + count_perfette - count_errori)/N_receptions,
  )

table_data

fig <- plot_ly(table_data, 
               x = ~positività, 
               y = ~efficienza,
               type = 'scatter', 
               mode = 'markers', 
               hovertemplate = paste('<i>Player</i>: %{text}',
                                     '<br><b>Positività</b>: %{x}',
                                     '<br><b>Efficienza</b>: %{y}',
                                     '<br><b>Ricezione</b>: %{marker.size}<extra></extra>'),
               color = ~positività,
               marker = list(size = ~N_receptions, sizemode = "area", sizeref = 0.005, opacity = 0.5),
               text = ~player_name
)

fig <- fig %>% layout(title = 'Qualità Ricezione',
                      xaxis = list(title = 'Positività', showgrid = TRUE),
                      yaxis = list(title = 'Efficienza', showgrid = TRUE)
)
fig

saveWidget(fig, "Ricezione.html")

Rendimento in Attacco

#  end_zone == 5
table_data <- px %>% 
  dplyr::filter(skill == "Attack", team == teamName) %>% 
  group_by(player_name) %>% 
  dplyr::summarize(
    N_attacks = n(),
    count_perfette = sum(evaluation_code == "#", na.rm = TRUE),
    count_positive = sum(evaluation_code == "+", na.rm = TRUE),
    count_escalamative = sum(evaluation_code == "!", na.rm = TRUE),
    count_negative = sum(evaluation_code == "-", na.rm = TRUE),
    count_errori = sum(evaluation_code == "=", na.rm = TRUE),
    positività = (count_positive + count_perfette)/N_attacks,
    efficienza = (count_positive + count_perfette - count_errori)/N_attacks,
  )

table_data

NA

fig <- plot_ly(table_data, 
               x = ~positività, 
               y = ~efficienza,
               type = 'scatter', 
               mode = 'markers', 
               hovertemplate = paste('<i>Player</i>: %{text}',
                                     '<br><b>Attacchi</b>: %{marker.size}<extra></extra>'),
               color = ~positività,
               marker = list(size = ~N_attacks, sizemode = "area", sizeref = 0.01, opacity = 0.5),
               text = ~player_name
)

fig <- fig %>% layout(title = 'Qualità Attacco',
                      xaxis = list(title = 'Positività', showgrid = FALSE),
                      yaxis = list(title = 'Efficienza', showgrid = FALSE)
)

fig

NA